Testing of Hypothesis

Hypothesis testing is a statistical method used to make inferences about population parameters based on a sample of data. It involves formulating a hypothesis about a population parameter and then using sample data to determine whether there is enough evidence to reject or not reject the null hypothesis.

Here are the key steps involved in hypothesis testing:

Formulate Hypotheses:

Null Hypothesis (H0): This is the default or status quo hypothesis. It often states that there is no effect or no difference.
Alternative Hypothesis (H1 or Ha): This is the hypothesis that the researcher aims to support, suggesting a specific effect or difference.
Choose Significance Level (α):

The significance level, denoted by α, is the probability of rejecting the null hypothesis when it is actually true. Commonly used values are 0.05, 0.01, or 0.10.
Collect and Analyze Data:

Collect a sample of data and analyze it using appropriate statistical methods.
Calculate Test Statistic:

Based on the sample data and the statistical test chosen, calculate a test statistic that will be used to assess the evidence against the null hypothesis.
Make a Decision:

Compare the calculated test statistic to a critical value (based on the chosen significance level) or determine the p-value. If the p-value is less than or equal to the significance level, the null hypothesis is rejected in favor of the alternative hypothesis.
Draw Conclusions:

Based on the decision in step 5, draw conclusions about the population parameter. If the null hypothesis is rejected, it suggests evidence in favor of the alternative hypothesis.
It's important to note that failing to reject the null hypothesis does not prove the null hypothesis to be true; it simply means that there is not enough evidence to reject it based on the given sample.